读取csv文件并在Python中返回data.frame
我有一个CSV文件， "value.txt"与以下内容：该文件的前几行是：
Date,"price","factor_1","factor_2" 2012-06-11,1600.20,1.255,1.548 2012-06-12,1610.02,1.258,1.554 2012-06-13,1618.07,1.249,1.552 2012-06-14,1624.40,1.253,1.556 2012-06-15,1626.15,1.258,1.552 2012-06-16,1626.15,1.263,1.558 2012-06-17,1626.15,1.264,1.572
在R中我们可以在使用中读取这个文件
price <- read.csv("value.txt")
这将返回一个data.frame我可以用于统计操作：
> price <- read.csv("value.txt") > price Date price factor_1 factor_2 1 2012-06-11 1600.20 1.255 1.548 2 2012-06-12 1610.02 1.258 1.554 3 2012-06-13 1618.07 1.249 1.552 4 2012-06-14 1624.40 1.253 1.556 5 2012-06-15 1626.15 1.258 1.552 6 2012-06-16 1626.15 1.263 1.558 7 2012-06-17 1626.15 1.264 1.572
是否有Pythonic方式获得相同的function？
大pandas的救援：
import pandas as pd print pd.read_csv('value.txt') Date price factor_1 factor_2 0 2012-06-11 1600.20 1.255 1.548 1 2012-06-12 1610.02 1.258 1.554 2 2012-06-13 1618.07 1.249 1.552 3 2012-06-14 1624.40 1.253 1.556 4 2012-06-15 1626.15 1.258 1.552 5 2012-06-16 1626.15 1.263 1.558 6 2012-06-17 1626.15 1.264 1.572
这返回类似于R'spandasDataFrame 。
这是使用Python内置csv模块的pandas库的替代scheme。
import csv from pprint import pprint with open('foo.csv', 'rb') as f: reader = csv.reader(f) headers = reader.next() column = {h:[] for h in headers} for row in reader: for h, v in zip(headers, row): column[h].append(v) pprint(column) # Pretty printer
将打印
{'Date': ['2012-06-11', '2012-06-12', '2012-06-13', '2012-06-14', '2012-06-15', '2012-06-16', '2012-06-17'], 'factor_1': ['1.255', '1.258', '1.249', '1.253', '1.258', '1.263', '1.264'], 'factor_2': ['1.548', '1.554', '1.552', '1.556', '1.552', '1.558', '1.572'], 'price': ['1600.20', '1610.02', '1618.07', '1624.40', '1626.15', '1626.15', '1626.15']}
您可以使用python标准库中的csv模块来操作CSV文件。
例：
import csv with open('some.csv', 'rb') as f: reader = csv.reader(f) for row in reader: print row
注意到很干净，但是：
import csv with open("value.txt", "r") as f: csv_reader = reader(f) num = ' ' for row in csv_reader: print num, '\t'.join(row) if num == ' ': num=0 num=num+1
虽然不是很紧凑，但却能完成这项工作：
Date price factor_1 factor_2 1 2012-06-11 1600.20 1.255 1.548 2 2012-06-12 1610.02 1.258 1.554 3 2012-06-13 1618.07 1.249 1.552 4 2012-06-14 1624.40 1.253 1.556 5 2012-06-15 1626.15 1.258 1.552 6 2012-06-16 1626.15 1.263 1.558 7 2012-06-17 1626.15 1.264 1.572
